Purpose: The ocular surface (OS) microbiome is influenced by various factors and impacts on ocular health. Understanding its composition and dynamics is crucial for developing targeted interventions for ocular diseases. This study aims to identify host variables, including physiological, environmental, and lifestyle (PEL) factors, that influence the ocular microbiome composition and establish valid associations between the ocular microbiome and health outcomes. Methods: The 16S rRNA gene sequencing was performed on OS samples collected from 135 healthy individuals using eSwab. DNA was extracted, libraries prepared, and PCR products purified and analyzed. PEL confounding factors were identified, and a cross-validation strategy using various bioinformatics methods including Machine learning was used to identify features that classify microbial profiles. Results: Nationality, allergy, sport practice, and eyeglasses usage are significant PEL confounding factors influencing the eye microbiome. Alpha-diversity analysis revealed significant differences between Spanish and Italian subjects (p-value < 0.001), with a median Shannon index of 1.05 for Spanish subjects and 0.59 for Italian subjects. Additionally, 8 microbial genera were significantly associated with eyeglass usage. Beta-diversity analysis indicated significant differences in microbial community composition based on nationality, age, sport, and eyeglasses usage. Differential abundance analysis identified several microbial genera associated with these PEL factors. The Support Vector Machine (SVM) model for Nationality achieved an accuracy of 100%, with an AUC-ROC score of 1.0, indicating excellent performance in classifying microbial profiles. Conclusion: This study underscores the importance of considering PEL factors when studying the ocular microbiome. Our findings highlight the complex interplay between environmental, lifestyle, and demographic factors in shaping the OS microbiome. Future research should further explore these interactions to develop personalized approaches for managing ocular health.

Rizzuto, V., Settino, M., Stroffolini, G., Covello, G., Vanags, J., Naccarato, M., et al. (2025). Ocular surface microbiome: Influences of physiological, environmental, and lifestyle factors. COMPUTERS IN BIOLOGY AND MEDICINE, 190 [10.1016/j.compbiomed.2025.110046].

Ocular surface microbiome: Influences of physiological, environmental, and lifestyle factors

Mazzotta, Cosimo;
2025-01-01

Abstract

Purpose: The ocular surface (OS) microbiome is influenced by various factors and impacts on ocular health. Understanding its composition and dynamics is crucial for developing targeted interventions for ocular diseases. This study aims to identify host variables, including physiological, environmental, and lifestyle (PEL) factors, that influence the ocular microbiome composition and establish valid associations between the ocular microbiome and health outcomes. Methods: The 16S rRNA gene sequencing was performed on OS samples collected from 135 healthy individuals using eSwab. DNA was extracted, libraries prepared, and PCR products purified and analyzed. PEL confounding factors were identified, and a cross-validation strategy using various bioinformatics methods including Machine learning was used to identify features that classify microbial profiles. Results: Nationality, allergy, sport practice, and eyeglasses usage are significant PEL confounding factors influencing the eye microbiome. Alpha-diversity analysis revealed significant differences between Spanish and Italian subjects (p-value < 0.001), with a median Shannon index of 1.05 for Spanish subjects and 0.59 for Italian subjects. Additionally, 8 microbial genera were significantly associated with eyeglass usage. Beta-diversity analysis indicated significant differences in microbial community composition based on nationality, age, sport, and eyeglasses usage. Differential abundance analysis identified several microbial genera associated with these PEL factors. The Support Vector Machine (SVM) model for Nationality achieved an accuracy of 100%, with an AUC-ROC score of 1.0, indicating excellent performance in classifying microbial profiles. Conclusion: This study underscores the importance of considering PEL factors when studying the ocular microbiome. Our findings highlight the complex interplay between environmental, lifestyle, and demographic factors in shaping the OS microbiome. Future research should further explore these interactions to develop personalized approaches for managing ocular health.
2025
Rizzuto, V., Settino, M., Stroffolini, G., Covello, G., Vanags, J., Naccarato, M., et al. (2025). Ocular surface microbiome: Influences of physiological, environmental, and lifestyle factors. COMPUTERS IN BIOLOGY AND MEDICINE, 190 [10.1016/j.compbiomed.2025.110046].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1290256